Skip to main content

Blockchain-based transfer learning for health screening with digital anthropometry from body images

Abstract

Anthropoid images encode reliable biometric information in abundance. Recent research on image-based screening drives this effort to investigate the feasibility of interpreting the inherent nutritional state from multidimensional human body images. However, anthropometric databases are becoming increasingly essential and grow in parallel to achieve efficient system designs. Typically, learning models on anthropometric databases require voluminous datasets, and obtaining huge volumes of labeled data for supervised algorithms can be challenging due to the time and cost complexity required to classify data points. This paper presents a novel imaging-based strategy in an augmented environment to quantify the human anthropometric features with blockchain-based transfer learning to generate a diagnosis report. It includes evaluating the attributes such as height, weight, waist, knee-length from an image using augmented reality and blockchain-based transfer learning for diagnostic accuracy. We developed a novel skeleton known as FETTLE with ARKit to determine the role of body measures for assessing nutritional conditions and body weight from human body images. It forms an instantly applicable technique aimed at evaluating children’s growth patterns all through their initial ages. The FETTLE app can also be operated on bedridden people as a screening mechanism to spot their risk of pressure ulcers and undernutrition, followed by a more structured examination. Our approach is superior in accuracy measures with consortium blockchain-based learning context with privacy-preserved medical data sharing at 1014 transactions per second and high-end user experience and interaction. Our framework is proved to gain about 97.26% validation accuracy on anthropoid images.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Availability of data and materials

Data analyzed in this study were a re-analysis of existing data, which are openly available at locations cited in the reference section. World Bank Health Nutrition and Population Statistics. Available online: https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics (accessed on 1 November 2020).

Code availability

Code is made available upon requests.

References

  • Affuso O, Pradhan L, Zhang C, Gao S, Wiener HW, Gower B, Allison DB (2018) A method for measuring human body composition using digital images. PLoS One 13(11):e0206430

    Article  Google Scholar 

  • Azhar F, Tjahjadi T (2014) Significant body point labeling and tracking. IEEE Trans Cybern 44(9):1673–1685

    Article  Google Scholar 

  • Ballard DH, Burton KR, Lakomkin N, Kim S, Rajiah P, Patel MJ, Whitman GJ (2020) The role of imaging in health screening: screening for specific conditions. Acad Radiol 28:548–563

    Article  Google Scholar 

  • Chen Y, Cheng ZQ, Lai C, Martin RR, Dang G (2015) Real-time reconstruction of an animating human body from a single depth camera. IEEE Trans Visual Comput Graph 22(8):2000–2011

    Article  Google Scholar 

  • Cheng KL, Tong RF, Tang M, Qian JY, Sarkis M (2015) Parametric human body reconstruction based on sparse key points. IEEE Trans vis Comput Graph 22(11):2467–2479

    Article  Google Scholar 

  • Cui PF, Yu Y, Lu WJ, Liu Y, Zhu HB (2017) Measurement and modeling of wireless off-body propagation characteristics under hospital environment at 6–8.5 GHz. IEEE Access 5:10915–10923

    Article  Google Scholar 

  • de Oliveira Rente P, Brites C, Ascenso J, Pereira F (2018) Graph-based static 3D point clouds geometry coding. IEEE Trans Multimed 21(2):284–299

    Article  Google Scholar 

  • Dey A, Jarvis G, Sandor C, Reitmayr G (2012) Tablet versus phone: depth perception in handheld augmented reality. In: 2012 IEEE international symposium on mixed and augmented reality (ISMAR). IEEE, pp 187–196

  • Edelman G, Alberink I (2010) Height measurements in images: how to deal with measurement uncertainty correlated to actual height. Law Probab Risk 9(2):91–102

    Article  Google Scholar 

  • Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976

    Article  Google Scholar 

  • Gedik OS, Alatan AA (2013) 3-D rigid body tracking using vision and depth sensors. IEEE Trans Cybern 43(5):1395–1405

    Article  Google Scholar 

  • Giachetti A, Lovato C, Piscitelli F, Milanese C, Zancanaro C (2014) Robust automatic measurement of 3D scanned models for the human body fat estimation. IEEE J Biomed Health Inform 19(2):660–667

    Article  Google Scholar 

  • Jayabal CP, Sathia Bhama PRK (2021) Performance analysis on Diversity Mining-based Proof of Work in bifolded consortium blockchain for Internet of Things consensus. Concurr Comput Pract Exper. https://doi.org/10.1002/cpe.6285

    Article  Google Scholar 

  • Jiang M, Guo G (2019) Bodyweight analysis from human body images. IEEE Trans Inf Forensics Secur 14(10):2676–2688

    Article  Google Scholar 

  • Juang CF, Chang CM, Wu JR, Lee D (2008) Computer vision-based human body segmentation and posture estimation. IEEE Trans Syst Man Cybern Part A Syst Hum 39(1):119–133

    Article  Google Scholar 

  • Li S, Lu H (2011) Arbitrary body segmentation with a novel graph cuts-based algorithm. IEEE Signal Process Lett 18(12):753–756

    Article  Google Scholar 

  • Li S, Lu H, Shao X (2014) Human body segmentation via data-driven graph cut. IEEE Trans Cybern 44(11):2099–2108

    Article  Google Scholar 

  • Liu Z, Huang J, Bu S, Han J, Tang X, Li X (2016) Template deformation-based 3-D reconstruction of full human body scans from low-cost depth cameras. IEEE Trans Cybern 47(3):695–708

    Article  Google Scholar 

  • Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G (2015) Transfer learning using computational intelligence: a survey. Knowl-Based Syst 80:14–23

    Article  Google Scholar 

  • Preethi ER, Farhana SR (2016) Deracinating deets from an image using FETTLE in international conference on current research in engineering and technology (ICET-16)

  • Preethi ER, Priya JC (2020) Digital anthropometry for health screening from an image using FETTLE App. In: International conference on paradigms on computing, communication and data sciences (PCCDS 2020). https://www.springer.com/gp/book/9789811575327

  • Preethi RE, Farhana SR, Lalitha SD (2016) Human attributes quantification from A 2D image using hale CANVAS app. Int J Innov Res Sci Eng Technol 5(3):4101–4105

    Google Scholar 

  • Prisacariu VA, Kähler O, Murray DW, Reid ID (2014) Real-time 3d tracking and reconstruction on mobile phones. IEEE Trans vis Comput Graph 21(5):557–570

    Article  Google Scholar 

  • Priya JC, Bhama PRS (2018) Disseminated and Decentred Blockchain secured Balloting: apropos to India. In: 2018 tenth international conference on advanced computing (ICoAC). IEEE, pp 323–327

  • Priya JC, Bhama PRS, Swarnalaxmi S, Safa AA, Elakkiya I (2018) Blockchain centered homomorphic encryption: a secure solution for E-balloting. In: International conference on computer networks, big data and IoT. Springer, Cham, pp 811–819

  • Priya JC, Ramanujan V, Rajeshwaran P,  Bhama S, Ponsy RK (2021) SG_BIoT: Integration of Blockchain in IoT Assisted Smart Grid for P2P Energy Trading. In: Dave M, Garg R, Dua M, Hussien J (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-7533-4_43

  • Sekhavat YA (2016) Privacy-preserving cloth try-on using mobile augmented reality. IEEE Trans Multimed 19(5):1041–1049

    Article  Google Scholar 

  • Song D, Tong R, Du J, Zhang Y, Jin Y (2018) Data-driven 3-D human body customization with a mobile device. IEEE Access 6:27939–27948

    Article  Google Scholar 

  • Sukno FM, Waddington JL, Whelan PF (2014) 3-D facial landmark localization with asymmetry patterns and shape regression from incomplete local features. IEEE Trans Cybern 45(9):1717–1730

    Article  Google Scholar 

  • Tong J, Zhou J, Liu L, Pan Z, Yan H (2012) Scanning 3d full human bodies using kinects. IEEE Trans vis Comput Graph 18(4):643–650

    Article  Google Scholar 

  • Tsitsoulis A, Bourbakis NG (2015) A methodology for extracting standing human bodies from single images. IEEE Trans Hum Mach Syst 45(3):327–338

    Article  Google Scholar 

  • ul Haque A, Ghani MS, Mahmood T (2020) Decentralized transfer learning using blockchain and IPFS for deep learning. In: 2020 International Conference on Information Networking (ICOIN). IEEE, pp 170–177

  • Wald J, Tateno K, Sturm J, Navab N, Tombari F (2018) Real-time fully incremental scene understanding on mobile platforms. IEEE Robot Autom Lett 3(4):3402–3409

    Article  Google Scholar 

  • World Bank Health Nutrition and Population Statistics (2020) https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics. Accessed 1 Nov 2020

  • Xu J, Glicksberg BS, Su C et al (2021) Federated learning for healthcare informatics. J Healthc Inform Res 5:1–19. https://doi.org/10.1007/s41666-020-00082-4

    Article  Google Scholar 

  • Zhang Y, Luo X, Yang W, Yu J (2019) Fragmentation guided human shape reconstruction. IEEE Access 7:45651–45661

    Article  Google Scholar 

  • Zhao T, Li S, Ngan KN, Wu F (2018) 3-D reconstruction of human body shape from a single commodity depth camera. IEEE Trans Multimed 21(1):114–123

    Article  Google Scholar 

Download references

Acknowledgements

This work concerns an expansion of a previous work presented by the same Corresponding Author in an International Conference on Paradigms of Computing, Communication and Data Sciences (PCCDS-2020) whose proceedings can be accessed at https://link.springer.com/book/10.1007%2F978-981-15-7533-4.

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to J. Chandra Priya or Tanupriya Choudhury.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Ethics approval

This article does not contain any studies involving human participants performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Priya, J.C., Choudhury, T., Khanna, A. et al. Blockchain-based transfer learning for health screening with digital anthropometry from body images. Netw Model Anal Health Inform Bioinforma 11, 23 (2022). https://doi.org/10.1007/s13721-022-00363-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-022-00363-5

Keywords

  • Stature finder
  • Weight quester
  • Scientific visualization
  • Blockchain
  • Transfer learning
  • Augmented reality
  • Digital anthropometry